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Image

Spatial Relationship

options(
  tidyverse.quiet      = TRUE,
  readr.show_col_types = FALSE
)

suppressPackageStartupMessages({
  library(EBImage)
  library(tidyverse)
  library(Rtsne)
  library(readxl)
})

meta <- read_excel("41467_2023_43458_MOESM4_ESM.xlsx")
boundaries <- read_csv("cell_boundaries.csv")
centroids <- read_csv("cbr.csv")
threepts <- read_csv("threepts.csv")

#head(meta)
#head(boundaries)
#head(centroids)
#head(threepts)

Umap of FFPE Cells

library(ggplot2)
library(readxl)
library(dplyr)

meta <- read_excel("41467_2023_43458_MOESM4_ESM.xlsx")

# Plot using UMAP coordinates and annotated cell types
ggplot(meta, aes(x = `UMAP-X`, y = `UMAP-Y`, color = Annotation)) +
  geom_point(size = 0.5, alpha = 0.7) +
  theme_minimal() +
  labs(title = "UMAP of FFPE Cells by Annotation",
       x = "UMAP dim. 1", y = "UMAP dim. 2") +
  theme(legend.position = "right")

UMAP (Uniform Manifold Approximation and Projection) reduces high-dimensional data into 2D, placing similar data points closer together. Compared to t-SNE, UMAP is faster, better preserves the overall global structure, and handles large datasets well. It is used because we cannot visualize high-dimensional data directly, and 2D projection helps to identify patterns and clusters.

FFPE (Formalin-Fixed Paraffin-Embedded) is a method to preserve tissue samples by fixing them with formalin and embedding them in paraffin. It is widely used in pathology, especially cancer research, because it preserves cellular structures over long periods.

The meta file contains UMAP coordinates (UMAP-X, UMAP-Y) and the annotated cell type for each cell. By plotting these on a UMAP plane, we can visualize how similar cells cluster and how different types are spatially separated.

Cell Type proportions

meta %>%
  count(Annotation) %>%
  ggplot(aes(x = reorder(Annotation, n), y = n, fill = Annotation)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme_minimal() +
  labs(title = "Cell Type Proportions",
       x = "Cell Type", y = "Count") +
  theme(legend.position = "none")

Start!!

library(tidyverse)
library(readr)

target_cell_types <- c(
  "Invasive_Tumor", "Macrophages_1", "Macrophages_2", 
  "CD4+_T_Cells", "CD8+_T_Cells", "B_Cells", 
  "DCIS_1", "DCIS_2", "Stromal", 
  "Myoepi_ACTA2+", "Myoepi_KRT15+"
)

base_path <- "/Users/hyungjin/Desktop/3_S1C/data3888/100"
cell_types <- list.files(base_path)

img_df <- map_df(cell_types, function(ct) {
  files <- list.files(file.path(base_path, ct), full.names = TRUE)
  tibble(
    path = files,
    cell_type = ct,
    cell_id = as.integer(str_match(basename(files), "cell_(\\d+)_")[,2])
  )
})

cbr <- read_csv(file.path(base_path, "cbr.csv"))
img_coords <- left_join(img_df, cbr, by = c("cell_id" = "index"))

Warning in left_join(img_df, cbr, by = c(cell_id = "index")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 859 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

img_coords_filtered <- img_coords %>%
  filter(cell_type %in% target_cell_types)

annotation_colors <- c(
  "Invasive_Tumor"  = "red",
  "Macrophages_1"   = "#FDB863",
  "Macrophages_2"   = "#E66101",
  "CD4+_T_Cells"    = "grey",
  "CD8+_T_Cells"    = "black",
  "B_Cells"         = "blue",
  "DCIS_1"          = "lightyellow",
  "DCIS_2"          = "yellow",
  "Myoepi_ACTA2+"   = "green",
  "Myoepi_KRT15+"   = "lightgreen",
  "Stromal" = "purple"
)

ggplot(img_coords_filtered, aes(x = `axis-1`, y = `axis-0`, color = cell_type)) +
  geom_point(size = 0.3, alpha = 0.2) +
  scale_color_manual(values = annotation_colors) +
  coord_fixed() +
  theme_void() +
  labs(title = "Spatial Plot of Key Cell Types")

Delete Stromal

library(tidyverse)
library(readr)

target_cell_types <- c(
  "Invasive_Tumor", "Macrophages_1", "Macrophages_2", 
  "CD4+_T_Cells", "CD8+_T_Cells", "B_Cells", 
  "DCIS_1", "DCIS_2", 
  "Myoepi_ACTA2+", "Myoepi_KRT15+"
)

base_path <- "/Users/hyungjin/Desktop/3_S1C/data3888/100"
cell_types <- list.files(base_path)

img_df <- map_df(cell_types, function(ct) {
  files <- list.files(file.path(base_path, ct), full.names = TRUE)
  tibble(
    path = files,
    cell_type = ct,
    cell_id = as.integer(str_match(basename(files), "cell_(\\d+)_")[,2])
  )
})

cbr <- read_csv(file.path(base_path, "cbr.csv"))
img_coords <- left_join(img_df, cbr, by = c("cell_id" = "index"))

Warning in left_join(img_df, cbr, by = c(cell_id = "index")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 859 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

img_coords_filtered <- img_coords %>%
  filter(cell_type %in% target_cell_types)

annotation_colors <- c(
  "Invasive_Tumor"  = "red",
  "Macrophages_1"   = "#FDB863",
  "Macrophages_2"   = "#E66101",
  "CD4+_T_Cells"    = "grey",
  "CD8+_T_Cells"    = "black",
  "B_Cells"         = "blue",
  "DCIS_1"          = "lightyellow",
  "DCIS_2"          = "yellow",
  "Myoepi_ACTA2+"   = "green",
  "Myoepi_KRT15+"   = "lightgreen"
)

ggplot(img_coords_filtered, aes(x = `axis-1`, y = `axis-0`, color = cell_type)) +
  geom_point(size = 0.3, alpha = 0.2) +
  scale_color_manual(values = annotation_colors) +
  coord_fixed() +
  theme_void() +
  labs(title = "Spatial Plot of Key Cell Types (Stromal X)")

Delete Stromal, Macophages1,2

library(tidyverse)
library(readr)

target_cell_types <- c(
  "Invasive_Tumor", "CD4+_T_Cells", "CD8+_T_Cells", "B_Cells", 
  "DCIS_1", "DCIS_2", "Myoepi_ACTA2+", "Myoepi_KRT15+")

base_path <- "/Users/hyungjin/Desktop/3_S1C/data3888/100"
cell_types <- list.files(base_path)

img_df <- map_df(cell_types, function(ct) {
  files <- list.files(file.path(base_path, ct), full.names = TRUE)
  tibble(
    path = files,
    cell_type = ct,
    cell_id = as.integer(str_match(basename(files), "cell_(\\d+)_")[,2])
  )
})

cbr <- read_csv(file.path(base_path, "cbr.csv"))
img_coords <- left_join(img_df, cbr, by = c("cell_id" = "index"))

Warning in left_join(img_df, cbr, by = c(cell_id = "index")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 859 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

img_coords_filtered <- img_coords %>%
  filter(cell_type %in% target_cell_types)

annotation_colors <- c(
  "Invasive_Tumor"  = "red",
  "CD4+_T_Cells"    = "grey",
  "CD8+_T_Cells"    = "black",
  "B_Cells"         = "blue",
  "DCIS_1"          = "lightyellow",
  "DCIS_2"          = "yellow",
  "Myoepi_ACTA2+"   = "green",
  "Myoepi_KRT15+"   = "lightgreen"
)

ggplot(img_coords_filtered, aes(x = `axis-1`, y = `axis-0`, color = cell_type)) +
  geom_point(size = 0.3, alpha = 0.2) +
  scale_color_manual(values = annotation_colors) +
  coord_fixed() +
  theme_void() +
  labs(title = "Spatial Plot of Key Cell Types (Stromal, Macrophages X)")

We characterized cellular heterogeneity at the boundary between DCIS (ductal carcinoma in situ) and invasive carcinoma. In H&E-stained breast carcinoma samples, DCIS_1 was enriched with myoepithelial markers (ACTA2, KRT15), whereas DCIS_2 showed reduced myoepithelial markers and increased invasive markers. In the invasive regions, myoepithelial cells were completely lost. These findings suggest that DCIS_2 represents a region with a high potential for invasive transition.

Invasive & DCIS_1,2

library(tidyverse)
library(readr)
target_cell_types <- c("Invasive_Tumor", "DCIS_1", "DCIS_2")
base_path <- "/Users/hyungjin/Desktop/3_S1C/data3888/100"
cell_types  <- list.files(base_path)
img_df <- map_df(cell_types, function(ct) {
  files <- list.files(file.path(base_path, ct), full.names = TRUE)
  tibble(
    path      = files,
    cell_type = ct,
    cell_id   = as.integer(str_match(basename(files), "cell_(\\d+)_")[, 2])
  )
})

cbr <- read_csv(file.path(base_path, "cbr.csv"))
img_coords <- left_join(img_df, cbr, by = c("cell_id" = "index"))

Warning in left_join(img_df, cbr, by = c(cell_id = "index")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 859 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

img_coords_filtered <- img_coords %>%
  filter(cell_type %in% target_cell_types)
annotation_colors <- c(
  "Invasive_Tumor" = "red",
  "DCIS_1"  = "yellow",
  "DCIS_2"  = "green"
)

ggplot(img_coords_filtered, aes(x = `axis-1`, y = `axis-0`, color = cell_type)) +
  geom_point(size = 0.3, alpha = 0.2) +
  scale_color_manual(values = annotation_colors) +
  coord_fixed() +
  theme_void() +
  labs(title = "Invasvie Tumor, DCIS1, DCIS2")